Skip to main content
SpringerLink
Log in

Energy-Efficient Scheduling for Portable Computers as Bi-Criteria Optimization Problem

  • Chapter
  • First Online:
Green IT Engineering: Concepts, Models, Complex Systems Architectures

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 74))

Abstract

Many of today’s portable computers are not inferior to the computing power of a desktop computer. The main problem of portable computers is short duration of activity of the device in standalone mode, while ensuring the quality of service, i.e., subjective user satisfaction. There is no single way of measuring subjective user satisfaction as the current quality of the service requirements conflicts with the requirements to ensure uptime portable computers. Power consumption model of the portable computer is presented as 3-level power graph. The developed model of the multitasking system’s scheduling in a portable computer battery life is based on the assumption that the problem under consideration belongs to the soft real-time subject. Synthesis of schedules multitasking system in a portable computer battery life is carried out by solving the bi-criteria optimization problem with the release of the Pareto-optimal solutions. These criteria includes minimum penalty for the decline in the quality of service and maximum battery life with the current profile of power consumption. For experimental investigation of the power consumption PCMark-7 and Microsoft Joulemeter were used. As a result of experiments it is found that during operation on portable computers minimum and maximum energy levels differ by more than 2 times. A time transition to a low or high voltage processor, respectively, can lead to a twofold increase in battery life. The latter is, of course, the most optimistic estimate, since an increase in battery life portable computers only 10–20 % will give the user quite noticeable and necessary advantages.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Urban, B., Shmakova, V., Lim, B., Roth, K.: Energy consumption of consumer electronics in U.S. homes in 2013. In: Consumer Technology Association. http://www.cta.tech/CorporateSite/media/environment/Energy-Consumption-of-Consumer-Electronics.pdf. (2014). Accessed 26 Dec 2015

  2. Carlson, B., Giolma, B.: Smartreflex. power and performance management technologies: reduced power consumption, optimized performance. In: Texas Instruments. http://www.ti.com/lit/wp/swpy015a/swpy015a.pdf. (2008). Accessed 29 Dec 2015

  3. Intel.: Enhanced IntelB® SpeedStepB® Technology for the IntelB® PentiumB® M Processor. http://download.intel.com/design/network/papers/30117401.pdf. (2004). Accessed 9 Jan 2016

  4. Larabel, M. (2006) Intel EIST SpeedStep - Phoronix. In: Phoronix.com. http://www.phoronix.com/scan.php?page=article&item=397&num=1. Accessed 12 Jan 2016

  5. Wikipedia.:Cool’n’Quiet. https://en.wikipedia.org/wiki/Cool%27n%27Quiet. (2004). Accessed 5 Jan 2016

  6. Snowdon, D., Ruocco, S., Heiser, G.: Power management and dynamic voltage scaling: myths and facts. In: Proceedings of the National ICT Australia and School of Computer Science and Engineering University of NSW. pp 15–19 (2005)

    Google Scholar 

  7. Badam, A., Skiani, E., Chandra, R., et al.: Software defined batteries. In: Proceedings of the 25th Symposium on Operating Systems Principles—SOSP ‘15 (2015). doi:10.1145/2815400.2815429

  8. Mudge, T.: Power: a first-class architectural design constraint. Computer 34, 52–58 (2001). doi:10.1109/2.917539

    Article  Google Scholar 

  9. Pering, T., Burd, T., Brodersen, R. The simulation and evaluation of dynamic voltage scaling algorithms. In: Proceedings of the 1998 international symposium on Low power electronics and design—ISLPED ‘98. (1998). doi:10.1145/280756.280790

  10. Pruhs, K., Uthaisombut, P., Woeginger, G.: Getting the best response for your erg. ACM transactions on algorithms 4, 1–17 (2008). doi:10.1145/1367064.1367078

    Article  MathSciNet  MATH  Google Scholar 

  11. Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms 3:49-es. (2007). doi:10.1145/1290672.1290686

  12. Bansal, N., Pruhs, K., Stein, C.: Speed scaling for weighted flow time. SIAM J. Comput. 39, 1294–1308 (2010). doi:10.1137/08072125x

    Article  MathSciNet  MATH  Google Scholar 

  13. Chan, H., Lam, T., Li, R.: Tradeoff between energy and throughput for online deadline scheduling. Sustain. Comput.: Inform. Syst. 1, 189–195 (2011). doi:10.1016/j.suscom.2011.05.002

    MathSciNet  MATH  Google Scholar 

  14. Andrew, L., Wierman, A., Tang, A.: Optimal speed scaling under arbitrary power functions. ACM SIGMETRICS Perform. Eval. Rev. 37, 39 (2009). doi:10.1145/1639562.1639576

    Article  Google Scholar 

  15. Irani, S., Shukla, S., Gupta, R.: Online strategies for dynamic power management in systems with multiple power-saving states. TECS 2, 325–346 (2003). doi:10.1145/860176.860180

    Article  Google Scholar 

  16. Irani, S., Shukla, S., Gupta, R.: Algorithms for power savings. ACM Trans. Algorithms 3:41-es (2007). doi:10.1145/1290672.1290678

  17. Kaur, T., Chana, I.: Energy Efficiency Techniques in Cloud Computing. CSUR 48, 1–46 (2015). doi:10.1145/2742488

    Article  Google Scholar 

  18. Zeng, H., Ellis, C., Lebeck, A.: Experiences in managing energy with ECOSystem. IEEE Pervasive Comput. 4, 62–68 (2005). doi:10.1109/mprv.2005.10

    Article  Google Scholar 

  19. Zeng, H., Ellis, C., Lebeck, A., Vahdat, A. Currentcy: a unifying abstraction for expressing energy management policies. In: Computer Science and Engineering. http://cseweb.ucsd.edu/~vahdat/papers/usenix03.pdf. Accessed 21 Dec 2015

  20. Neugebauer R, Mcauley, D.: Energy is just another resource: energy accounting and energy pricing in the Nemesis OS. In: Proceedings Eighth Workshop on Hot Topics in Operating Systems. (2001). doi:10.1109/hotos.2001.990063

  21. Sachs, D., Yuan, W., Hughes, C., et al.: GRACE: a hierarchical adaptation framework for saving energy. 2004–2409

    Google Scholar 

  22. Vardhan, V., Yuan, W., Harris, A., et al.: GRACE-2: integrating fine-grained application adaptation with global adaptation for saving energy. IJES 4, 152 (2009). doi:10.1504/ijes.2009.027939

    Article  Google Scholar 

  23. Jeffay, K., Zhang, H.: Readings in multimedia computing and networking. Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  24. Flinn, J., Satyanarayanan, M.: Managing battery lifetime with energy-aware adaptation. ACM Trans. Comput. Syst. 22, 137–179 (2004). doi:10.1145/986533.986534

    Article  Google Scholar 

  25. windows.microsoft.com.: Conserving battery power—Windows Help. http://windows.microsoft.com/en-us/windows/conserving-battery-power#1TC=windows-7. (2013) Accessed 6 Jan 2016

  26. European Commission.: Energy-efficient computing systems, dynamic adaptation of quality of service and approximate computing.pp. 1–44 (2014)

    Google Scholar 

  27. Pinedo, M.: Scheduling. (2012). doi:10.1007/978-1-4614-2361-4

    Article  MathSciNet  Google Scholar 

  28. Kaldewey, T., Lin, C., Brandt, S. Firm real-time processing in an integrated real-time system. In: Work in Progress Session of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium vol. 4 (2006)

    Google Scholar 

  29. Cucu-Grosjean, L., Buffet, O.: Global multiprocessor real-time scheduling as a constraint satisfaction problem. In: International Conference on Parallel Processing Workshops. (2009). doi:10.1109/icppw.2009.31

  30. Huang, D., Shi, J.: Quality of service scheduling in wireless multimedia communications. In: Proceedings on IEEE VTS 53rd Vehicular Technology Conference, Spring 2001 (Cat No01CH37202). doi:10.1109/vetecs.2001.945092

  31. Buttazzo, G., Lipari, G., Abeni, L.: Elastic task model for adaptive rate control. In: Proceedings on 19th IEEE Real-Time Systems Symposium (Cat No98CB36279). doi:10.1109/real.1998.739754

  32. Kuo, T., Mok, A.: Load adjustment in adaptive real-time systems. In Proceedings Twelfth Real-Time Systems Symposium. (1991) doi:10.1109/real.1991.160369

  33. Futuremark.com.: Test your PC and Android device. http://www.futuremark.com/benchmarks/pcmark. (2015). Accessed 8 Nov 2015

  34. Research at Microsoft.: Joulemeter: computational energy measurement and optimization. http://research.microsoft.com/en-us/projects/joulemeter/. (2014). Accessed 3 Dec 2015

Download references

Author information

Authors and Affiliations

  1. National Aerospace University “KhAI”, 17 Chkalova Street, Kharkiv, Ukraine

    Igor Turkin & Aleksandr Vdovitchenko

Authors
  1. Igor Turkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aleksandr Vdovitchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Igor Turkin .

Editor information

Editors and Affiliations

  1. KhAI 503, Department of Computer Systems & Network KhAI 503, Kharkiv, Ukraine

    Vyacheslav Kharchenko

  2. PMBSSU, 68th, Dept of Intelligent Information Systems PMBSSU, 68th, Mykolaiv, Ukraine

    Yuriy Kondratenko

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory Sys Res Intit of Polish Acad of Science, Warszawa, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Turkin, I., Vdovitchenko, A. (2017). Energy-Efficient Scheduling for Portable Computers as Bi-Criteria Optimization Problem. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-44162-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44162-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44161-0

  • Online ISBN: 978-3-319-44162-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation